Daniel A Jacobson
Daniel A Jacobson
My lab focuses on the development and subsequent application of mathematical, statistical and computational methods to biological datasets in order to yield new insights into complex biological systems. Our approaches include the use of Network Theory and Topology Discovery/Clustering, Wavelet Theory, Machine & Deep Learning (amongst others: iterative Random Forests, Deep Neural Networks, etc.) and Linear Algebra (primarily as applied to large-scale multivariate modeling), together with traditional and more advanced computing architectures, such MPI parallelization and Apache Spark. We make use of various programming languages including C, Python, Perl, Scala and R. Areas of Statistics of particular interest to my lab include the use of both frequentist (parametric and non-parametric) and Bayesian methods as well as the development of new methods for Genome-Wide Association Studies (GWAS) and Phenome-Wide Associations Studies (PheWAS). These mathematical and statistical methods are applied to various population and (meta)multiomics data sets (Genomics, Phylogenomics, Transcriptomics, Proteomics, Metabolomics, Microbiomics, Viriomics, Phytobiomics, Chemiomics, etc.) individually as well as in combination in an attempt to better understand the functional relationships as well as biosynthesis, signaling, transcriptional, translational, degradation and kinetic regulatory networks at play in biological organisms and communities.
Many of our projects center around studying systems in involved the Center for Bioenergy Innovation (CBI), Plant-Microbial Interfaces (PMI) and Crassulacean Acid Metabolism (CAM) Biodesign programs at ORNL. However, we have a broad view of biological complexity and evolution that stretches from viruses to microbes to plants to humans (including cancer and neuroscience).
ORNL is home to some of the world’s largest supercomputers. My lab uses petascale computing to analyze and model complex biological systems and are actively involved in the development of exascale applications for biology. Thus, there are excellent opportunities to be involved in the cutting edge of computational biology and supercomputing.
As interdisciplinary and multidisciplinary efforts are more and more critical for scientific discovery, we do maintain a wide network of collaborations from all around the world.
Weighill DA, Jacobson DA. (2016) Network Metamodeling: The Effect of Correlation Metric Choice on Phylogenomic and Transcriptomic Network Topology. Book Chapter, Adv Biochem Eng Biotechnol. DOI: 10.1007/10_2016_46
Paul E. Abraham, Hengfu Yin, Anne M. Borland, Deborah Weighill, Sung Don Lim, Henrique Cestari De Paoli, Nancy Engle, Piet C. Jones, Ryan Agh, David J. Weston, Stan D. Wullschleger, Timothy Tschaplinski, Daniel Jacobson, John C. Cushman, Robert L. Hettich, Gerald A. Tuskan, Xiaohan Yang. (2016) Temporal dynamics of transcripts, proteins, and metabolites that define crassulacean acid metabolism in Agave. Nature Plants 2, Article number: 16178 (2016) doi:10.1038/nplants.2016.178
Kaijie Zheng, Xiaoping Wang, Deborah Weighill, Hao-Bo Guo, Meng Xie, Yongil Yang, Jun Yang, Shucai Wang, Daniel Jacobson, Hong Guo, Wellington Muchero, Gerald Tuskan, and Jin-Gui Chen. (2016) Characterization of DWARF14 Genes in Populus. Scientific Reports 6:21593 | doi: 10.1038/srep21593
Coetzee, C., Brand, J., Jacobson, D., & Du Toit, W. J. (2016). Sensory effect of acetaldehyde on the perception of 3‐mercaptohexan‐1‐ol and 3‐isobutyl‐2 methoxypyrazine. Australian Journal of Grape and Wine Research. | doi: 10.1111/ajgw.12206
Young, P., Eyeghe-Bickong, H. A., du Plessis, K., Alexandersson, E., Jacobson, D. A., Coetzee, Z. A., Deloire, A., Vivier, M. A. (2015). Grapevine plasticity in response to an altered microclimate: Sauvignon Blanc modulates specific metabolites in response to increased berry exposure. Plant Physiology . doi:10.1104/pp.15.01775
Setati ME, Jacobson D and Bauer FF (2015). Sequence-based analysis of the Vitis vinifera L. cv Cabernet Sauvignon grape must mycobiome in three South African vineyards employing distinct agronomic systems. Front. Microbiol. 6:1358. doi: 10.3389/fmicb.2015.01358
Weighill DA, Jacobson DA. (2015) 3-way Networks: Application of Hypergraphs for Modelling Increased Complexity in Comparative Genomics. PLoS Comput Biol 11(3): e1004079. doi: 10.1371/journal.pcbi.1004079.
Monforte, A. R., Jacobson, D., & Silva Ferreira, A. C. (2015). Chemiomics: Network Reconstruction and Kinetics of Port Wine Aging. Journal of agricultural and food chemistry, 63(9), 2576-2581.
Whitener, M. E. B., Carlin, S., Jacobson, D., Weighill, D., Divol, B., Conterno, L., ... & Vrhovsek, U. (2015). Early fermentation volatile metabolite profile of non-Saccharomyces yeasts in red and white grape must: a targeted approach. LWT-Food Science and Technology.
Coetzee, C., Brand, J., Emerton, G., Jacobson, D., Silva Ferreira, A.C. and du Toit, W.J. (2015), Sensory interaction between 3-mercaptohexan-1-ol, 3-isobutyl-2-methoxypyrazine and oxidation-related compounds. Australian Journal of Grape and Wine Research. doi: 10.1111/ajgw.12133
Alexandersson E, Jacobson D, Vivier M, Weckwerth W and Andreasson E. "Field-omics" – understanding large-scale molecular data from field crops. Front. Plant Sci. 5:286, 2014
Fairbairn S, Smit A, Jacobson D, Prior B, Bauer F. Environmental Stress and Aroma Production During Wine Fermentation. S. Afr. J. Enol. Vitic., 35:168-177, 2014
Bengtsson T, Weighill D, Proux-Wéra E, Levander F, Resjö S, Burra DD, Moushib LI, Hedley PE, Liljeroth E, Jacobson D, Alexandersson E and E Andreasson. Proteomics and Transcriptomics of the BABA-Induced Resistance Response in Potato Using a Novel Functional Annotation Approach. BMC Genomics. 15:315, 2014
van Wyngaard, E., Brand, J., Jacobson, D. & du Toit, W. Sensory interaction between 3-mercaptohexan-1-ol and 2-isobutyl-3-methoxypyrazine in dearomatised Sauvignon Blanc wine. Aust. J. Grape Wine Res. 20, 178–185, 2014
Marx IJ, van Wyk N, Smit S, Jacobson D, Viljoen-Bloom M, Volschenk H. Comparative secretome analysis of Trichoderma asperellum S4F8 and Trichoderma reesei Rut C30 during solid-state fermentation on sugarcane bagasse. Biotechnology for Biofuels. 6, pp 172, 2013
Jacobson D., Monforte, A.R., Silva Ferreira A.C., Untangling the chemistry of Port wine aging with the use of GC-FID, multivariate statistics and network reconstruction, Journal of Agricultural and Food Chemistry, 61 (10), pp 2513–2521, 2013
Setati ME, Jacobson D, Andong UC, Bauer F, The vineyard yeast microbiome, a mixed model microbial map., PloS One, 7, e52609, 2012
Young P, Lashbrooke J, Alexandersson E, Jacobson D, Moser C, Velasco R, Vivier M, The genes and enzymes of the carotenoid metabolic pathway in Vitis vinifera, BMC Genomics, 13, 243, 2012
Jacobson D, Emerton G, GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network, BMC Bioinformatics, 13, 197, 2012
Styger G, Jacobson D. Prior B. Bauer F, Genetic analysis of the metabolic pathways responsible for aroma metabolite production by Saccharomyces cerevisiae., Applied microbiology and biotechnology, 1-14, 2012
Rossouw D, Jolly N, Jacobson D, Bauer F, The effect of scale on gene expression: commercial versus laboratory wine fermentations, Applied microbiology and biotechnology, 93, 1207-1219, 2012
Rossouw D, Jacobson D, Bauer F, Transcriptional regulation and the diversification of metabolism in wine yeast strains, Genetics, 190, 251-261, 2012
Bester M, Jacobson D, Bauer F, Many Saccharomyces cerevisiae Cell Wall Protein Encoding Genes Are Coregulated by Mss11, but Cellular Adhesion Phenotypes Appear Only Flo Protein Dependent., G3, 2, 131-141, 2012
Sharathchandra R, Stander C, Jacobson D, Ndimba B, Vivier M, Proteomic analysis of grape berry cell cultures reveals that developmentally regulated ripening related processes can be studied using cultured cells., PloS One, 6, e14708, 2011
Alexandersson E, Becker J, Jacobson D, Nguema-Ona E, Steyn C, Denby K, Vivier M, Constitutive expression of a grapevine polygalacturonase-inhibiting protein affects gene expression and cell wall properties in uninfected tobacco., BMC Research notes, 4, 493, 2011
Styger G, Jacobson D, Bauer F, Identifying genes that impact on aroma profiles produced by Saccharomyces cerevisiae and the production of higher alcohols., Applied microbiology and biotechnology, 91, 713-730, 2011
Rossouw D, van den Dool A, Jacobson D, Bauer F, Comparative transcriptomic and proteomic profiling of industrial wine yeast strains. Applied and environmental microbiology, 76, 3911-3912, 2010
Opara UL, Jacobson D, Al-Saady NA, Analysis of genetic diversity in banana cultivars (Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic, hierarchical clustering and principal component analyses., Journal of Zhejiang University. Science. B, 11, 332-341, 2010
O'Neill K, Garcia A, Schwegmann A, Jimenez R, Jacobson D, Hermjakob H, OntoDas - a tool for facilitating the construction of complex queries to the Gene Ontology. BMC Bioinformatics, 9, 437, 2008
Anagnostopoulos, A.V, and D. Jacobson. It's a knockout! 1996. Trends in Genetics 12: 236.
Jacobson, D. and A. Anagnostopoulos. 1996. Trends in Genetics 12: 117-118.
D. Jacobson 1994. The World Wide Web for biologists, Protein Science 3: 2159-2161.
Woychik, R.P., Wassom, J.S., Kingsbury, D. and D.A. Jacobson. TBASE: a computerized database for transgenic animals and targeted mutations, 1993. Nature 636: 375-376.
Jacobson, K.B., Arlinghaus, H.F., Schmitt, H., Sachleben, R.A., Brown, G.M., Thonnard, N., Sloop, F.V., Foote, R.S., Larimer, F.W., Woychik, R.P., England, M.W., Burchett, K.L. and D.A. Jacobson. 1991. An approach to the use of stable isotopes for DNA sequencing, Genomics 9: 51-59.
McCarthy, J. F., Jacobson, D. A., Shugart, L. R. & Jimenez, B. D. Pre-exposure to 3-methylcholanthrene increases benzo[a]pyrene adducts on DNA of bluegill sunfish. Marine Environmental Research 28, 323–328 (1989).